GUI-Guided Test Script Repair for Mobile Apps

Pan, Minxue; Xu, Tongtong; Pei, Yu; Zhong, Li; Zhang, Tian; Li, Xuandong

doi:10.1109/tse.2020.3007664

Cited by 22 publications

(6 citation statements)

References 56 publications

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“…Pan et al [11] developed a framework named GUI-guided test script repair for mobile applications. Here, an effective meter scheme was employed for the repair of bugs and faults in the GUI test scripts.…”

Section: Related Literature Surveymentioning

confidence: 99%

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An Effective GDP-LSTM and SDQL-Based Finite State Testing of GUI

Kumar,

Nitin,

Yadav

2024

Applied Sciences

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The Graphical User Interface (GUI) is the most promising factor in the Software Development Lifecycle (SDL), which allows the users to interact with the system. To ensure user-friendliness, GUI Testing (GT) is required. The traditional testing techniques attained flawed results due to having inappropriate functions. Hence, Global Decaying Probabilistic Long Short-Term Memory (GDP-LSTM) and Standard Deviation Q-Learning (SDQL)-based automatic testing for GUI are proposed as solutions. Initially, the Test Case (TC) and GUI are extracted from the historical data and are subjected to Region of Interest (ROI) analysis. Here, an appropriate ROI is analyzed by Module Coupling Slice (MCS), and it is fed into Hadoop Parallelization (HP). Now, Spectral Kernelized Gaussian Clustering (SKGC) and Non-Linear Elite Guided Optimized Ant Colony (NE-GO-AC) are used to perform mapping and reducing, respectively. Likewise, the parallelized output is utilized to construct the Document Object Model (DOM) tree. Then, the attributes are extracted and given to the GDP-LSTM classifier that effectively predicts whether GUIs are desirable or undesirable. Then, the undesirable results are inputted into a SDQL-based deviation analysis. If the deviation is low, it is assumed as an update; otherwise, it is considered as an error. The experimental analysis depicted that the proposed system attained high dominance with 98.89% accuracy in the prevailing models.

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Section: Related Literature Surveymentioning

confidence: 99%

“…A ← ℘ eps (11) where ρ indicates the GK parameter. The DP in the affinity matrix is connected to the points, which fall in the radius of ℘ eps .…”

Section: Hadoop Parallelizationmentioning

confidence: 99%

An Effective GDP-LSTM and SDQL-Based Finite State Testing of GUI

Kumar,

Nitin,

Yadav

2024

Applied Sciences

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“…Модель предсказывает как место действия, так и тип действия как распределения вероятностей. Таким образом, их кросс-энтропийные потери по сравнению с основной правдой (действия, выполняемые людьми) подходят для оптимизации модели [7].…”

Section: полност ью подклю ченны й слойunclassified

Building a Training Model to Generate Test Data for Gui Testing

Sarycheva,

Belov

2022

НО ТН (SR TS)

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Графический интерфейс является неотъемлемой частью программного обеспечения на большинстве основных платформ, включая Android, и представляет широкий интерес для многих исследователей. Конечные пользователи взаимодействуют с приложением в зависимости от того, что они хотят делать с приложением и что они видят в его графическом интерфейсе. Поскольку разные приложения часто используют общие шаблоны проектирования пользовательского интерфейса, интуитивно понятно, что способ взаимодействия людей с графическим интерфейсом можно обобщить для разных приложений. Цель модели взаимодействия состоит в том, чтобы зафиксировать такие обобщенные модели взаимодействия. В данном исследовании будет рассмотрен автоматизированный генератор входных данных с графическим интерфейсом, который может изучать, как люди взаимодействуют с мобильными приложениями, а затем использовать ее для управления генерацией входных данных, имитируя поведение человека. Причина, по которой эта стратегия может улучшить тестовое покрытие, заключается в том, что важные состояния, которые пользователи предпочитают посещать и с большим количеством возможных входных данных, изучаются чаще. При ограниченном времени и бюджете трата времени на эти важные состояния может эффективнее улучшить тестовое покрытие.

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“…Droix [27] employs a search-based approach to generate patches that fix crashes in Android apps. AppEvolve [20] analyzes existing updates in other apps to generate patches that fix issues in Android apps caused by an API evolving. METER [14] leverages computer vision techniques to fix broken GUI test scripts during app evolution.…”

Section: Automated Program Repair For Android Appsmentioning

confidence: 99%

Detecting and fixing data loss issues in Android apps

Guo

Dong

Shen

et al. 2022

Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis

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Android apps are event-driven, and their execution is often interrupted by external events. This interruption can cause data loss issues that annoy users. For instance, when the screen is rotated, the current app page will be destroyed and recreated. If the app state is improperly preserved, user data will be lost. In this work, we present an approach and tool iFixDataloss that automatically detects and fixes data loss issues in Android apps. To achieve this, we identify scenarios in which data loss issues may occur by analyzing the Android life cycle, developing strategies to reveal data loss issues, and designing patch templates to fix them. Our experiments on 66 Android apps show iFixDataloss detected 374 data loss issues (284 of them were previously unknown) and successfully generated patches for 188 of the 374 issues. Out of 20 submitted patches, 16 have been accepted by developers. In comparison with state-of-the-art techniques, iFixDataloss performed significantly better in terms of the number of detected data loss issues and the quality of generated patches.

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GUI-Guided Test Script Repair for Mobile Apps

Cited by 22 publications

References 56 publications

An Effective GDP-LSTM and SDQL-Based Finite State Testing of GUI

An Effective GDP-LSTM and SDQL-Based Finite State Testing of GUI

Building a Training Model to Generate Test Data for Gui Testing

Detecting and fixing data loss issues in Android apps

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